“‘Model-Based RL’ Tag”,2018-12-12

Bibliography for tag reinforcement-learning/model, most recent first: 7 related tags, 207 annotations, & 30 links (parent).

• See Also
• Gwern
  • “Resorting Media Ratings”, Gwern2015
• Links
  • “Centaur: a Foundation Model of Human Cognition”, Binz et al 2024
  • “Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making”, Li et al 2024
  • “Interpretable Contrastive Monte Carlo Tree Search Reasoning”, Gao et al 2024
  • “OpenAI Co-Founder Sutskever’s New Safety-Focused AI Startup SSI Raises $1 Billion”, Cai et al 2024
  • “The Brain Simulates Actions and Their Consequences during REM Sleep”, Senzai & Scanziani2024
  • “Solving Path of Exile Item Crafting With Value Iteration”, Britz2024
  • “Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models”, Denison et al 2024
  • “DT-VIN: Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning”, Wang et al 2024
  • “MCTSr: Accessing GPT-4 Level Mathematical Olympiad Solutions via Monte Carlo Tree Self-Refine With LLaMA-3-8B”, Zhang et al 2024
  • “Safety Alignment Should Be Made More Than Just a Few Tokens Deep”, Qi et al 2024
  • “Can Language Models Serve As Text-Based World Simulators?”, Wang et al 2024
  • “Evaluating the World Model Implicit in a Generative Model”, Vafa et al 2024
  • “OmegaPRM: Improve Mathematical Reasoning in Language Models by Automated Process Supervision”, Luo et al 2024
  • “Diffusion On Syntax Trees For Program Synthesis”, Kapur et al 2024
  • “DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches With TikZ”, Belouadi et al 2024
  • “DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data”, Xin et al 2024
  • “Amit’s A^✱ Pages”, Patel2024
  • “From r to Q^✱: Your Language Model Is Secretly a Q-Function”, Rafailov et al 2024
  • “Identifying General Reaction Conditions by Bandit Optimization”, Wang et al 2024b
  • “Gradient-Based Planning With World Models”, V et al 2023
  • “ReCoRe: Regularized Contrastive Representation Learning of World Model”, Poudel et al 2023
  • “Can a Transformer Represent a Kalman Filter?”, Goel & Bartlett2023
  • “Self-Supervised Behavior Cloned Transformers Are Path Crawlers for Text Games”, Wang & Jansen2023
  • “Why Won’t OpenAI Say What the Q^✱ Algorithm Is? Supposed AI Breakthroughs Are Frequently Veiled in Secrecy, Hindering Scientific Consensus”, Hao2023
  • “Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations”, Hong et al 2023
  • “The Neural Basis of Mental Navigation in Rats: A Brain–machine Interface Demonstrates Volitional Control of Hippocampal Activity”, Coulter & Kemere2023
  • “Volitional Activation of Remote Place Representations With a Hippocampal Brain–machine Interface”, Lai et al 2023
  • “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”, Zhang et al 2023
  • “Self-AIXI: Self-Predictive Universal AI”, Catt et al 2023
  • “Othello Is Solved”, Takizawa2023
  • “Course Correcting Koopman Representations”, Fathi et al 2023
  • “Predictive Auxiliary Objectives in Deep RL Mimic Learning in the Brain”, Fang & Stachenfeld2023
  • “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Zhou et al 2023
  • “Comparative Study of Model-Based and Model-Free Reinforcement Learning Control Performance in HVAC Systems”, Gao & Wang2023
  • “Learning to Model the World With Language”, Lin et al 2023
  • “Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations”, Chen et al 2023
  • “Fighting Uncertainty With Gradients: Offline Reinforcement Learning via Diffusion Score Matching”, Suh et al 2023
  • “Improving Long-Horizon Imitation Through Instruction Prediction”, Hejna et al 2023
  • “When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming (CDHF)”, Mozannar et al 2023
  • “Long-Term Value of Exploration: Measurements, Findings and Algorithms”, Su et al 2023
  • “Emergence of Belief-Like Representations through Reinforcement Learning”, Hennig et al 2023
  • “Six Experiments in Action Minimization”, Greydanus2023
  • “Finding Paths of Least Action With Gradient Descent”, Greydanus2023
  • “MimicPlay: Long-Horizon Imitation Learning by Watching Human Play”, Wang et al 2023
  • “Graph Schemas As Abstractions for Transfer Learning, Inference, and Planning”, Guntupalli et al 2023
  • “John Carmack’s ‘Different Path’ to Artificial General Intelligence”, Carmack2023
  • “DreamerV3: Mastering Diverse Domains through World Models”, Hafner et al 2023
  • “Merging Enzymatic and Synthetic Chemistry With Computational Synthesis Planning”, Levin et al 2022
  • “PALMER: Perception-Action Loop With Memory for Long-Horizon Planning”, Beker et al 2022
  • “Space Is a Latent [CSCG] Sequence: Structured Sequence Learning As a Unified Theory of Representation in the Hippocampus”, Raju et al 2022
  • “CICERO: Human-Level Play in the Game of Diplomacy by Combining Language Models With Strategic Reasoning”, Bakhtin et al 2022
  • “Online Learning and Bandits With Queried Hints”, Bhaskara et al 2022
  • “E3B: Exploration via Elliptical Episodic Bonuses”, Henaff et al 2022
  • “Creating a Dynamic Quadrupedal Robotic Goalkeeper With Reinforcement Learning”, Huang et al 2022
  • “Top-Down Design of Protein Nanomaterials With Reinforcement Learning”, Lutz et al 2022
  • “Simplifying Model-Based RL: Learning Representations, Latent-Space Models, and Policies With One Objective (ALM)”, Ghugare et al 2022
  • “IRIS: Transformers Are Sample-Efficient World Models”, Micheli et al 2022
  • “LGE: Cell-Free Latent Go-Explore”, Gallouédec & Dellandréa2022
  • “LaTTe: Language Trajectory TransformEr”, Bucker et al 2022
  • “PI-ARS: Accelerating Evolution-Learned Visual-Locomotion With Predictive Information Representations”, Lee et al 2022
  • “Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Dalle et al 2022
  • “Spatial Representation by Ramping Activity of Neurons in the Retrohippocampal Cortex”, Tennant et al 2022
  • “Inner Monologue: Embodied Reasoning through Planning With Language Models”, Huang et al 2022
  • “LM-Nav: Robotic Navigation With Large Pre-Trained Models of Language, Vision, and Action”, Shah et al 2022
  • “DayDreamer: World Models for Physical Robot Learning”, Wu et al 2022
  • “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Baker et al 2022
  • “GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, Peng et al 2022
  • “BYOL-Explore: Exploration by Bootstrapped Prediction”, Guo et al 2022
  • “Director: Deep Hierarchical Planning from Pixels”, Hafner et al 2022
  • “Flexible Diffusion Modeling of Long Videos”, Harvey et al 2022
  • “Housekeep: Tidying Virtual Households Using Commonsense Reasoning”, Kant et al 2022
  • “Semantic Exploration from Language Abstractions and Pretrained Representations”, Tam et al 2022
  • “Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning”, Valassakis et al 2022
  • “Do As I Can, Not As I Say (SayCan): Grounding Language in Robotic Affordances”, Ahn et al 2022
  • “Reinforcement Learning With Action-Free Pre-Training from Videos”, Seo et al 2022
  • “On-The-Fly Strategy Adaptation for Ad-Hoc Agent Coordination”, Zand et al 2022
  • “VAPO: Affordance Learning from Play for Sample-Efficient Policy Learning”, Borja-Diaz et al 2022
  • “Learning Synthetic Environments and Reward Networks for Reinforcement Learning”, Ferreira et al 2022
  • “How to Build a Cognitive Map: Insights from Models of the Hippocampal Formation”, Whittington et al 2022
  • “LID: Pre-Trained Language Models for Interactive Decision-Making”, Li et al 2022
  • “Rotting Infinitely Many-Armed Bandits”, Kim et al 2022
  • “Language Models As Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents”, Huang et al 2022
  • “What Is the Point of Computers? A Question for Pure Mathematicians”, Buzzard2021
  • “An Experimental Design Perspective on Model-Based Reinforcement Learning”, Mehta et al 2021
  • “Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates”, Kantack2021
  • “Learning Representations for Pixel-Based Control: What Matters and Why?”, Tomar et al 2021
  • “Learning Behaviors through Physics-Driven Latent Imagination”, Richard et al 2021
  • “Is Bang-Bang Control All You Need? Solving Continuous Control With Bernoulli Policies”, Seyde et al 2021
  • “Skill Induction and Planning With Latent Language”, Sharma et al 2021
  • “Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks”, Wu et al 2021
  • “TrufLL: Learning Natural Language Generation from Scratch”, Donati et al 2021
  • “Dropout’s Dream Land: Generalization from Learned Simulators to Reality”, Wellmer & Kwok2021
  • “FitVid: Overfitting in Pixel-Level Video Prediction”, Babaeizadeh et al 2021
  • “Brax—A Differentiable Physics Engine for Large Scale Rigid Body Simulation”, Freeman et al 2021
  • “A Graph Placement Methodology for Fast Chip Design”, Mirhoseini et al 2021
  • “Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning”, Lipovetzky2021
  • “The Whole Prefrontal Cortex Is Premotor Cortex”, Fine & Hayden2021
  • “PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World”, Zellers et al 2021
  • “Constructions in Combinatorics via Neural Networks”, Wagner2021
  • “Machine Translation Decoding beyond Beam Search”, Leblond et al 2021
  • “Learning What To Do by Simulating the Past”, Lindner et al 2021
  • “Waymo Simulated Driving Behavior in Reconstructed Fatal Crashes within an Autonomous Vehicle Operating Domain”, Scanlon et al 2021
  • “Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing”, Brunnbauer et al 2021
  • “Replaying Real Life: How the Waymo Driver Avoids Fatal Human Crashes”, Waymo2021
  • “Learning Chess Blindfolded: Evaluating Language Models on State Tracking”, Toshniwal et al 2021
  • “COMBO: Conservative Offline Model-Based Policy Optimization”, Yu et al 2021
  • “A^✱ Search Without Expansions: Learning Heuristic Functions With Deep Q-Networks”, Agostinelli et al 2021
  • “ViNG: Learning Open-World Navigation With Visual Goals”, Shah et al 2020
  • “Inductive Biases for Deep Learning of Higher-Level Cognition”, Goyal & Bengio2020
  • “Multimodal Dynamics Modeling for Off-Road Autonomous Vehicles”, Tremblay et al 2020
  • “Targeting for Long-Term Outcomes”, Yang et al 2020
  • “What Are the Statistical Limits of Offline RL With Linear Function Approximation?”, Wang et al 2020
  • “A Time Leap Challenge for SAT Solving”, Fichte et al 2020
  • “The Overfitted Brain: Dreams Evolved to Assist Generalization”, Hoel2020
  • “RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning”, Gulcehre et al 2020
  • “Mathematical Reasoning via Self-Supervised Skip-Tree Training”, Rabe et al 2020
  • “MOPO: Model-Based Offline Policy Optimization”, Yu et al 2020
  • “Learning to Simulate Dynamic Environments With GameGAN”, Kim et al 2020
  • “Planning to Explore via Self-Supervised World Models”, Sekar et al 2020
  • “Learning to Simulate Dynamic Environments With GameGAN [Homepage]”, Kim et al 2020
  • “Reinforcement Learning With Augmented Data”, Laskin et al 2020
  • “Learning to Fly via Deep Model-Based Reinforcement Learning”, Becker-Ehmck et al 2020
  • “Introducing Dreamer: Scalable Reinforcement Learning Using World Models”, Hafner2020
  • “Reinforcement Learning for Combinatorial Optimization: A Survey”, Mazyavkina et al 2020
  • “Learning to Prove Theorems by Learning to Generate Theorems”, Wang & Deng2020
  • “The Gambler’s Problem and Beyond”, Wang et al 2019
  • “Combining Q-Learning and Search With Amortized Value Estimates”, Hamrick et al 2019
  • “Dream to Control: Learning Behaviors by Latent Imagination”, Hafner et al 2019
  • “Approximate Inference in Discrete Distributions With Monte Carlo Tree Search and Value Functions”, Buesing et al 2019
  • “Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?”, Du et al 2019
  • “Designing Agent Incentives to Avoid Reward Tampering”, Everitt et al 2019
  • “An Application of Reinforcement Learning to Aerobatic Helicopter Flight”, Abbeel et al 2019
  • “When to Trust Your Model: Model-Based Policy Optimization (MOPO)”, Janner et al 2019
  • “VISR: Fast Task Inference With Variational Intrinsic Successor Features”, Hansen et al 2019
  • “Learning to Reason in Large Theories without Imitation”, Bansal et al 2019
  • “Biasing MCTS With Features for General Games”, Soemers et al 2019
  • “Bayesian Layers: A Module for Neural Network Uncertainty”, Tran et al 2018
  • “PlaNet: Learning Latent Dynamics for Planning from Pixels”, Hafner et al 2018
  • “Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning”, Foerster et al 2018
  • “Human-Like Playtesting With Deep Learning”, Gudmundsson et al 2018
  • “General Value Function Networks”, Schlegel et al 2018
  • “Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search”, Zela et al 2018
  • “The Alignment Problem for Bayesian History-Based Reinforcement Learners”, Everitt & Hutter2018
  • “Neural Scene Representation and Rendering”, Eslami et al 2018
  • “Deep Reinforcement Learning in a Handful of Trials Using Probabilistic Dynamics Models”, Kurtl et al 2018
  • “Mining Gold from Implicit Models to Improve Likelihood-Free Inference”, Brehmer et al 2018
  • “Learning to Optimize Tensor Programs”, Chen et al 2018
  • “Reinforcement Learning and Control As Probabilistic Inference: Tutorial and Review”, Levine2018
  • “Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Hadash et al 2018
  • “World Models”, Ha & Schmidhuber2018
  • “Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling”, Riquelme et al 2018
  • “Differentiable Dynamic Programming for Structured Prediction and Attention”, Mensch & Blondel2018
  • “How to Explore Chemical Space Using Algorithms and Automation”, Gromski et al 2018
  • “Planning Chemical Syntheses With Deep Neural Networks and Symbolic AI”, Segler et al 2018
  • “Generalization Guides Human Exploration in Vast Decision Spaces”, Wu et al 2018
  • “Safe Policy Search With Gaussian Process Models”, Polymenakos et al 2017
  • “Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics”, Chatzilygeroudis & Mouret2017
  • “Analogical-Based Bayesian Optimization”, Le et al 2017
  • “A Game-Theoretic Analysis of the Off-Switch Game”, Wängberg et al 2017
  • “Neural Network Dynamics for Model-Based Deep Reinforcement Learning With Model-Free Fine-Tuning”, Nagabandi et al 2017
  • “Learning Transferable Architectures for Scalable Image Recognition”, Zoph et al 2017
  • “Learning Model-Based Planning from Scratch”, Pascanu et al 2017
  • “Value Prediction Network”, Oh et al 2017
  • “Path Integral Networks: End-To-End Differentiable Optimal Control”, Okada et al 2017
  • “Visual Semantic Planning Using Deep Successor Representations”, Zhu et al 2017
  • “AIXIjs: A Software Demo for General Reinforcement Learning”, Aslanides2017
  • “Metacontrol for Adaptive Imagination-Based Optimization”, Hamrick et al 2017
  • “DeepArchitect: Automatically Designing and Training Deep Architectures”, Negrinho & Gordon2017
  • “Stochastic Constraint Programming As Reinforcement Learning”, Prestwich et al 2017
  • “Recurrent Environment Simulators”, Chiappa et al 2017
  • “Prediction and Control With Temporal Segment Models”, Mishra et al 2017
  • “Rotting Bandits”, Levine et al 2017
  • “The Kelly Coin-Flipping Game: Exact Solutions”, Gwern et al 2017
  • “The Hippocampus As a Predictive Map”, Stachenfeld et al 2017
  • “The Predictron: End-To-End Learning and Planning”, Silver et al 2016
  • “Model-Based Adversarial Imitation Learning”, Baram et al 2016
  • “DeepMath: Deep Sequence Models for Premise Selection”, Alemi et al 2016
  • “Value Iteration Networks”, Tamar et al 2016
  • “On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, Schmidhuber2015
  • “Classical Planning Algorithms on the Atari Video Games”, Lipovetzky et al 2015
  • “Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-Armed Bandit Problem With Multiple Plays”, Komiyama et al 2015
  • “Compress and Control”, Veness et al 2014
  • “Learning to Win by Reading Manuals in a Monte-Carlo Framework”, Branavan et al 2014
  • “Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science”, Clark2013
  • “Model-Based Bayesian Exploration”, Dearden et al 2013
  • “PUCT: Continuous Upper Confidence Trees With Polynomial Exploration-Consistency”, Auger et al 2013
  • “Planning As Satisfiability: Heuristics”, Rintanen2012
  • “Width and Serialization of Classical Planning Problems”, Lipovetzky & Geffner2012
  • “An Empirical Evaluation of Thompson Sampling”, Chapelle & Li2011
  • “Monte-Carlo Planning in Large POMDPs”, Silver & Veness2010
  • “A Monte Carlo AIXI Approximation”, Veness et al 2009
  • “Evolution And Episodic Memory: An Analysis And Demonstration Of A Social Function Of Episodic Recollection”, Klein et al 2009
  • “Resilient Machines Through Continuous Self-Modeling”, Bongard et al 2006
  • “Policy Mining: Learning Decision Policies from Fixed Sets of Data”, Zadrozny2003
  • “The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions”, Schmidhuber2002
  • “Iterative Widening”, Cazenave2001
  • “Abstract Proof Search”, Cazenave2000
  • “A Critique of Pure Reason”, McDermott1987
  • “Human Window on the World”, Michie1985
  • “Why the Law of Effect Will Not Go Away”, Dennett1974
  • “Getting the World Record in HATETRIS”
  • “Solving Probabilistic Tic-Tac-Toe”, Abraham2024
  • “Approximate Bayes Optimal Policy Search Using Neural Networks”
  • “Embodying Addiction: A Predictive Processing Account”
  • “Introducing ‘Computer Use’, a New Claude 3.5 Sonnet, and Claude 3.5 Haiku”, Anthropic2024
  • “Developing a Computer Use Model”, Anthropic2024
  • “Best-Of-n With Misaligned Reward Models for Math Reasoning”
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